Bolstering Cloud Security with Security with Real-Time Stem using Hybrid-Rule based and ML Insights

Dalwai, Arbaz Adib (2024) Bolstering Cloud Security with Security with Real-Time Stem using Hybrid-Rule based and ML Insights. Masters thesis, Dublin, National College of Ireland.

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Abstract

The dynamic cloud environments often rely on traditional rule-based detection systems alone to detect the sophisticated threats like distributed denial of service (DDoS) and phishing which sometimes fall inadequate in terms of adaptability required in modern day detection. The research tries to bridge the gap between the academic researches and practical applications by contributing a scalable and robust detection framework in modern cloud infrastructure. This research aims to design and implement a real-time hybrid detection mechanism on a cloud platform- Amazon Web Services (AWS) that would integrate the security information and event management (SIEM), intrusion detection system (IDS) and machine learning (ML) models to detect and classify the cyber threats efficiently. Attack simulations were conducted to generate real-time logs which were monitored through the Suricata IDS, followed by the log processing in elastic-search, logstash and kibana (ELK) stack. Ensemble learning models like Random Forest (RF) and XGBoost were deployed to complement the rule-based detections and all this was presented in visual forms in real-time without significant delays. This was proven by an average detection time of 0.5 milliseconds, demonstrating the systems suitability for real-world conditions. The framework tends to bridge the gap between conceptual and practical deployments with the implementation of real-time hybrid detection system in a cloud environment.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Verma, Rohit UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	18 Jul 2025 10:30
Last Modified:	18 Jul 2025 10:30
URI:	https://norma.ncirl.ie/id/eprint/8196

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